Blending (Ensemble Method)

Overview Blending is an ensemble machine learning technique that uses a held-out validation set to train a meta-learner. It is often considered a simpler, "leakage-free" variation of Stacking.

The Process 1. Split: Divide the training data into two disjoint sets: Train (70%) and Holdout (30%). 2. Level 1: Train base models (e.g., XGBoost, Neural Net) on the 70% Train set. 3. Predict: Use these models to make predictions on the 30% Holdout set. 4. Level 2: Create a new dataset where the features are the specific predictions from Level 1, and the target is the real target. 5. Meta-Learn: Train a final model (e.g., Linear Regression) on this new dataset.

Pros & Cons

• Pros: Prevents information leakage because the meta-learner never sees the data used to train the base models. Extremely robust against overfitting.
• Cons: Less data efficient. You sacrifice 30% of your training data just to train the meta-learner, whereas Stacking uses 100% via Cross-Validation.